class_docstr: Feature Extractor is proposed in  FAN Ref [1]
  
  Ref [1]: Focusing Attention: Towards Accurate Text Recognition in Neural Images ICCV-2017
class_docstr_tok: ["Feature","Extractor","is","proposed","in","FAN","Ref","[","1","]","Ref","[","1","]",":","Focusing","Attention",":","Towards","Accurate","Text","Recognition","in","Neural","Images","ICCV-2017"]
docstr: Args:
      in_channels (int): input channel
      output_channel (int): output channel
docstr_tok: ["Args",":","in_channels","(","int",")",":","input","channel","output_channel","(","int",")",":","output","channel"]
code: def __init__(self, in_channels, out_channels=512):
      """
  
      Args:
          in_channels (int): input channel
          output_channel (int): output channel
      """
      super(ResNet32, self).__init__()
      self.out_channels = out_channels
      self.ConvNet = ResNet(
          in_channels, out_channels, BasicBlock, [1, 2, 5, 3]
      )
  
file_path: PaddleOCR/ppocr/modeling/backbones/rec_resnet_32.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle.nn
label_desc: Use image node_id 1 to create a new ResNet32 object from inherited base classes: nn with example: obj = ResNet32(in_channels, out_channels)